def _image_wider_than_requested_aspect_ratio():
crop_height = tf.cast(
tf.rint(crop_proportion * image_height_float), tf.int32)
crop_width = tf.cast(tf.rint(
crop_proportion * aspect_ratio *
image_height_float), tf.int32)
return crop_height, crop_width
def _smallest_size_at_least(height, width, smallest_side):
"""Computes new shape with the smallest side equal to `smallest_side`.
Computes new shape with the smallest side equal to `smallest_side` while
preserving the original aspect ratio.
Args:
height: an int32 scalar tensor indicating the current height.
width: an int32 scalar tensor indicating the current width.
smallest_side: A python integer or scalar `Tensor` indicating the size of
the smallest side after resize.
Returns:
new_height: an int32 scalar tensor indicating the new height.
new_width: and int32 scalar tensor indicating the new width.
"""
smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)
height = tf.to_float(height)
width = tf.to_float(width)
smallest_side = tf.to_float(smallest_side)
scale = tf.cond(tf.greater(height, width),
lambda: smallest_side / width,
lambda: smallest_side / height)
new_height = tf.to_int32(tf.rint(height * scale))
new_width = tf.to_int32(tf.rint(width * scale))
return new_height, new_width
